CN115150246A - Mass real-time Internet of things-oriented chain loading method based on novel nested chain architecture - Google Patents
Mass real-time Internet of things-oriented chain loading method based on novel nested chain architecture Download PDFInfo
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Abstract
The invention discloses a massive real-time Internet of things-oriented uplink method based on a novel nested chain architecture, and provides a nested chain architecture with a parent chain and a plurality of child chains coexisting, namely the parent chain maintains a small-scale node number and is mainly responsible for collecting important information for IoT data verification, identity information recording, global data management and the like; the sub-chains are multiple in number, multiple in nodes, high in flexibility and expansibility, and used for receiving and storing local IoT data and providing processing before uplink for the application data under the chain, so that massive data of the application network under the chain can be completely, timely and efficiently uploaded. Meanwhile, the dynamic deployment method for the application network subchain nodes based on the nested chain design is used for researching how to dynamically sense and investigate malicious nodes, efficiently selecting and replacing parent link access nodes so as to ensure the legality and safety of the nodes and efficient uplink of data and realize high-availability subchain dynamic access.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a massive real-time Internet of things-oriented chain linking method based on a novel nested chain architecture.
Background
The block chain is a novel application mode combining distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and other computer technologies, and is widely applied to a plurality of frontier fields by virtue of characteristics of decentralization, no tampering, traceability, anonymity and the like. With the rapid development of internet of things (IoT) and 5G technologies, massive real-time data generated in application scenarios such as internet of vehicles and telemedicine become a key challenge for the current blockchain technology to land on the ground.
A block chain system such as superhedger Fabric in alliance has higher throughput and lower latency, and can implement authentication of node identity and access control of data. However, the expansibility, especially the number of nodes, is limited compared with the public chain, and as the number of nodes of the added blockchain increases, the system overhead increases sharply and the performance decreases sharply.
Disclosure of Invention
The invention provides a massive real-time internet of things oriented uplink method based on a novel nested chain architecture, aiming at the problem that the number of block chain links of a alliance chain is increased, the system overhead is increased rapidly, and the performance is reduced rapidly, so that the under-chain information is difficult to cooperate with the on-chain data.
In order to achieve the above purpose, the invention provides the following technical scheme:
a massive real-time Internet of things-oriented chain loading method based on a novel nested chain architecture is characterized in that a block chain system comprises a single parent chain and a plurality of sub-chains, and the constituent nodes of the parent chain and the sub-chains are divided into two types: the common node is only responsible for the local data management function; the communication node has the same function as a common node and also has a cross-link communication function; the communication nodes in the parent chain and the child chain together construct a communication channel, namely a relay chain, which is used for data exchange among the chains; the sub-chain coexists with the parent chain in a nested structure and is used for realizing dynamic sensing of sub-chain communication nodes based on machine learning for large-scale application data under the link, searching malicious nodes, efficiently selecting and replacing parent chain access nodes and realizing high-availability sub-chain dynamic access.
Furthermore, in the selection process of the sub-chain access node, the improved GCN algorithm is used for sensing and classifying the nodes, so that the high-efficiency nodes in the sub-chain are screened out for inter-chain communication.
Further, the selecting process of the child chain access node is as follows:
s31, graph construction:
constructing transaction graphs by using the characteristic attributes of the nodes in the block chain and the transaction information among the nodes, wherein each transaction graph is a directed graph which is composed of the nodes and edges and contains edge information and is used as the input of a neural network model;
s32, graph learning:
the GCN model initially inputs a matrix generated by graph construction, and then node states are updated through hierarchical propagation;
s33, node classification and parent chain cooperative evaluation:
obtaining the state vector of each node after T iterations in the last layer of the GCN model
Wherein t is the output characteristic number of each node, namely the node category number; for each node application
Mapping the model output value into a probability value to obtain the classification probability of each node:
the vector represents the probability that the ith node is in the t categories respectively, namely the probability values of the nodes which are used as an access node, a candidate node, a common node and a low-efficiency node respectively, and the maximum probability is taken as the node type to obtain the classification probability of each node; meanwhile, the access node selected by the child chain is evaluated by the parent chain according to the historical update information of the node, and if the history of the node is used as the times T of the low-efficiency node>Threshold Th, the parent chain requests the child chain to replace the access node from the list of candidate nodes.
Further, the graph construction process mainly comprises the following key parameters:
node feature matrix
The characteristic attribute of the kth node is expressed as:
f 1 number of transactions initiated for the round, f 2 Number of transactions initiated for previous round, s 1 Is the current node type, s 2 Is the type of the node of the previous wheel;
weight matrix
A transaction weight matrix of type r, the weight matrix being defined by transaction weights
Constructing, namely the times of constructing a certain edge in the transaction graph, namely the transaction times from the node i to the node j in the current round;
transaction delay matrix
By
Constructing, wherein the value represents the transaction delay level of the type r from the node i to the node j in the M transactions in the current round, the order of the transaction delay level is preset to be n, namely, n delay levels are provided in total, and the extreme value t of all transaction delays in the current round is taken max 、t min Push and press
Grading uniformly; average transaction delay of type r between node i and node j in the current round
Determining t from a rank-dividing range cur The grade is obtained
Network delay matrix
By
Constructing, wherein the value represents the delay level of the network environment when the transaction with the type r is carried out from the node i to the node j in the M transactions in the current round, the delay level is divided into n levels, and the network ping value t of each transaction with the type r in the current round is counted max 、t min Push and press
Uniformly grading, calculating average network time delay, and determining grade to obtain
Further, in the graph learning process, the propagation model of node forward update is defined as:
wherein
Representing the hidden state of the ith node in the l +1 layer neural network;
alpha and beta are respectively the weight of transaction delay and network delay, and alpha + beta =1;
σ is a ReLU activation function;
normalization factor
A node set representing that r type transaction is carried out between the current round and the node i;
Furthermore, in the graph learning process, the iteration times of the state vector is defined as T, namely l is less than or equal to T; the input to the model layer I is represented as
Wherein H (0) = V, forward propagation makes a total of T state updates.
The chain linking method based on the novel nested chain architecture and oriented to the mass real-time Internet of things comprises the following operation flows:
s1, original data processing: the method comprises the steps that the Internet of things equipment sends original data to a sub chain through a gateway, firstly, the gateway conducts identity verification on the Internet of things equipment, and after the verification is passed, preprocessing is conducted on the original data before chain linking and the original data are sent to the sub chain;
s2, request sending:
the sub-chain initiates a communication request to the parent chain and sends the request to a system sequencing service;
s3, selecting a communication transaction node:
defining a sub-chain to realize node selection every M times of transactions, and selecting a high-efficiency node by the sub-chain according to situation perception of nodes in the chain to serve as a communication node accessed to a parent chain; the parent chain has a fixed communication node for each sub-chain;
s4, data sending:
the sequencing service establishes a channel and adds the communication nodes selected by the two parties into the channel to form a relay chain, and the sub-chain communication nodes read specified data from the chain and submit the data to the relay chain after being processed by a specific intelligent contract;
s5, data verification and confirmation:
the two communication nodes in the relay chain reach consensus, data is written into local blocks of the two communication nodes, and the communication node of the parent chain reads the data from the local, and submits the data to the chain and reaches consensus after verifying the integrity and consistency of the received data, so as to complete data exchange;
s6, transaction information storage:
after each transaction is completed, the transaction event is stored in the sub-chain in a key-value pair mode for next node selection, and node updating information is simultaneously sent to the relay chain and then backed up in the parent chain for evaluation of the parent chain on the sub-chain node.
Further, in step S4, the sub-chain uploads only the hash digest of the processed data of the internet of things to the main chain.
Further, in step S6, the transaction information attributes are as follows: < sender, receiver, type, transaction delay, network delay >, sender represents a transaction initiator, receiver represents a transaction receiver, type represents a transaction type, transaction delay represents a transaction delay (in ms), and network delay represents a network delay.
Further, if the transaction passes through the sub-chain node selection process, the type update event of each node is stored in the sub-chain in a key-value pair form, the attributes include < ID, category, transactions >, the ID represents the node ID, the category represents the node type after the update, and the transactions represent the number of transactions initiated by the node in the current round.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a novel nested chain architecture-based mass real-time Internet of things-oriented uplink method, which provides a nested chain architecture with a parent chain and a plurality of sub-chains coexisting, namely the parent chain maintains a small-scale node number and is mainly responsible for collecting important information for IoT data verification, identity information recording, global data management and the like; the sub-chains are multiple in number, multiple in nodes, high in flexibility and expansibility, and used for receiving and storing local IoT data and providing processing before uplink for the application data under the chain, so that massive data of the application network under the chain can be completely, timely and efficiently uploaded. Meanwhile, the dynamic deployment method for the application network subchain nodes based on the nested chain design is used for researching how to dynamically sense and investigate malicious nodes, efficiently selecting and replacing parent link access nodes so as to ensure the legality and safety of the nodes and efficient uplink of data and realize high-availability subchain dynamic access.
Compared with the prior art, the massive real-time Internet of things-oriented chain loading method based on the novel nested chain architecture has the advantages that: (1) And mass real-time data are efficiently processed, and the pressure of a father chain is relieved. The sub-chain coexists with the parent chain in a nested structure and is used for butt joint of large-scale application data under the chain, the dimensionality reduction and the frequency reduction of the data are achieved, and the possibility is provided for data collaboration under the chain. (2) The method has the advantages of having a high-performance stable subchain, and ensuring the integrity of data information and the high efficiency of data uplink. A situation awareness method based on machine learning is adopted to dynamically control subchain nodes, a deep neural network is designed to mine potential features, low-efficiency nodes are screened, and the legality of authentication nodes, high expansibility of subchains and stable communication among parent subchains are guaranteed. (3) Aiming at the singleness of the evaluation nodes of only the sub-chain, in order to more accurately classify the nodes, a scheme for cooperatively evaluating the parent chain and the sub-chain is designed to improve the efficiency of identifying the inefficient nodes.
Drawings
In order to more clearly illustrate the embodiments of the present application or technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a flowchart of a method provided in an embodiment of the present invention.
Fig. 2 is a schematic diagram of a nested chain architecture according to an embodiment of the present invention.
Fig. 3 is a flowchart of a node-aware algorithm provided in an embodiment of the present invention.
Fig. 4 is a node feature vector provided in the embodiment of the present invention.
Fig. 5 is a weight matrix provided in an embodiment of the invention.
Detailed Description
For a better understanding of the present solution, the method of the present invention is described in detail below with reference to the accompanying drawings.
Aiming at the mass scale and high real-time performance of the current block chain network application data, the invention designs a nested chain architecture with the cooperation of parent and child chains, as shown in fig. 2. The basic idea is that a plurality of small-scale block chains are adopted in parallel to avoid performance loss caused by continuous addition of nodes in a single chain, and meanwhile, independence of parent-child chains in the system and information exchange among the parent-child chains are guaranteed, and the block chain system can be completely, timely and efficiently uploaded by application data.
In order to better manage the whole system and adapt to the needs of the actual scene of the internet of things, the system consists of a single parent chain (Main chain) and a plurality of Sub-chains (Sub chains), each chain can independently bear the function of a small-scale block chain, and the constituent nodes of the system are mainly divided into two types: the common node is only responsible for the local data management function; the communication nodes have a cross-link communication function besides the same function as a common node, and the communication nodes in the parent Chain and the child Chain can jointly construct a communication channel, which is called a Relay Chain (Relay Chain) in the system and used for data exchange between the chains.
The invention comprehensively considers conditions such as application network communication states, transaction data and the like, provides a massive real-time Internet of things-oriented chain linking method based on a novel nested chain architecture, realizes dynamic sensing of a sub-chain communication node based on machine learning, optimizes node intelligent selection and model updating, ensures high efficiency of communication between a parent chain and a sub-chain, and builds a stable and highly expandable sub-chain.
The operation flow of the block chain system of the present invention is shown in fig. 1, and specifically includes the following steps:
step 1: raw data processing
The method comprises the steps that the Internet of things equipment sends original data to a sub chain through a gateway, firstly, the gateway conducts identity verification on the Internet of things equipment, and after the verification is passed, preprocessing is conducted on the original data before chain linking and the original data are sent to the sub chain.
Step 2: request sending
The child chain initiates a communication request to the parent chain and sends the request to a system ordering service (controlled by a system administrator);
and step 3: communication (transaction) node selection
Defining a sub-chain to realize node selection every M times of transactions, and selecting a high-efficiency node by the sub-chain according to situation perception of nodes in the chain to serve as a communication node accessed to a parent chain; the parent chain has a fixed communication node for each child chain. Aiming at the selection process of the optimized access node of the sub-chain, the invention utilizes an improved GCN (graph convolutional neural network) algorithm to sense and classify the nodes, as shown in figure 3, thereby effectively screening out efficient nodes in the sub-chain in time for inter-chain communication and ensuring that application data is complete and fast to chain.
3.1 map construction
And constructing a transaction graph by using the characteristic attributes of the nodes in the block chain and the transaction information among the nodes, wherein the transaction graph is used as the input of a neural network model, and each transaction graph is a directed graph G = (V, E) which is composed of the nodes and edges and contains edge information, wherein V is the nodes, and E is the edges. The total number of nodes N = | V |, the edge E = { (N) i ，n j ，t)|n i ，n j E.g., V) represents a transaction initiated by the node i to the node j, wherein the transaction type is t (such as asset transfer, ledger query and the like).
The graph construction process mainly comprises the following key parameters:
node feature matrix
The characteristic attribute of the kth node is represented as:
f 1 number of transactions initiated for the round, f 2 Number of transactions initiated for previous round, s 1 Is the current node type, s 2 Is of the previous wheel node type.
Weight matrix
A transaction weight matrix of type r, the weight matrix being defined by transaction weights
And constructing, namely the number of times a certain edge in the transaction graph is constructed (the transaction number from the node i to the node j in the current round).
Transaction delay matrix
By
Constructing, wherein the value represents the transaction delay level of the type r from the node i to the node j in M transactions in the current round, the order of the transaction delay level is preset to be n (namely, n delay levels are provided in total), and the extreme value t of all transaction delays in the current round is taken max 、t min Push-button
And (5) grading uniformly. Average transaction delay of type r between node i and node j in the current round
Determining t from a rank-dividing range cur The grade is obtained
Network delay matrix
By
Construct the value representing the type r from node i to node j in M transactions of the current roundThe network environment delay level of the transaction is divided into n levels, and the network ping value t of each transaction with the type r in the current round is counted max 、t min The same procedure as above is as follows
Uniformly grading, calculating average network time delay, and determining grade to obtain
3.2 Picture learning
The GCN model is initially input as a matrix generated by graph construction, then node states are updated through hierarchical propagation, and a propagation model of node forward update is defined as:
wherein
Representing the hidden state of the ith node in the l +1 layer neural network;
alpha and beta are respectively the weight of transaction delay and network delay, and alpha + beta =1;
σ is a ReLU activation function;
normalization factor
(
A node set representing that r type transaction is performed with the node i in the current round);
And defining the iteration times of the state vector as T, namely l is less than or equal to T. Model (model)The input of the l-th layer can be expressed as
Wherein H (0) = V, forward propagation makes a total of T state updates.
3.3 node Classification and parent chain Co-evaluation
Obtaining the state vector of each node after T iterations in the last layer of the GCN model
Where t is the output feature number of each node, i.e., the node class number. For each node application
Mapping the output value of the model into a probability value to obtain the classification probability of each node
The vector represents the probability that the ith node is in the t categories respectively, namely the probability values of the nodes which are respectively used as an access node, a candidate node, a common node and an inefficient node, and the maximum probability is taken as the node type. Meanwhile, the access node selected by the child chain is evaluated by the parent chain according to the historical update information of the node, and if the history of the node is used as the times T of the low-efficiency node>Threshold Th, the parent chain requests the child chain to replace the access node from the list of candidate nodes.
And step 3: data transmission
And the sequencing service establishes a channel and adds the communication nodes selected by the two parties into the channel to form a relay chain. The sub-chain communication nodes read the designated data from the chain, and submit the data to the relay chain after the data is processed by the specific intelligent contract. In order to avoid the overhead caused by the transmission of a large amount of data on the chain, the sub-chain only uploads the processed hash abstract of the data of the Internet of things to the main chain.
And 4, step 4: data verification and validation
The two communication nodes in the relay chain reach consensus, data is written into local blocks of the two communication nodes, and the communication node of the parent chain reads the data from the local blocks, submits the data to the chain after verifying the integrity and consistency of the received data and reaches the consensus, so that data exchange is completed.
And 5: transaction information storage
After each transaction is completed, the transaction event is stored in the sub-chain in a key-value pair mode for next node selection, and the attributes are as follows: < sender, receiver, type, transaction delay, network delay >, respectively, represent a transaction initiator, a transaction receiver, a transaction type, a transaction delay (in ms), and a network delay (in ms). If the transaction passes through the process of selecting the child chain nodes, the type updating event (in the form of key value pairs) of each node is also stored in the child chain, and the attributes include < ID, category, transactions >, which respectively represent the node ID, the node type after the update, and the number of transactions initiated by the node in the current round. The node update information is also sent to the relay chain and then backed up in the parent chain for evaluation of child chain nodes by the parent chain.
Examples
First, the device identity is verified by the gateway before the internet of things device sends the raw data to the blockchain, and if the internet of things device is not registered on the sub-chain, the gateway registers a new identity for the device by a CA (certificate authority) contacting the blockchain. After the identity authentication is passed, original data are integrated into a json key value pair form by a gateway, the original data are encrypted and stored under a chain for data which are difficult to process, and the hash abstract and the storage address of the original data are sent to the chain.
After receiving the data, the sub-chain sends a communication request to the parent chain, meanwhile, high-efficiency nodes are selected in the chain for communication, and if the transaction is the Kth time (K < M) in the round, the sub-chain directly takes the access node selected by the previous round as a communication node; if the transaction is the Mth time, node classification and selection are carried out:
the basic parameters are assumed to be as follows: in the nested chain architecture, one sub-chain is 3 nodes, a node set N = { a, B, C }, a transaction period M =10, a transaction type r =2, a delay level order N =5, a weight α =0.8, β =0.2, an iteration number T =2, and a threshold Th =2.
Assume that the 10 transaction events in this round are as follows:
<A,B,1,28,56><A,C,1,34,69><B,A,2,150,45><C,A,1,25, 47><A,B,2,197,77><C,B,1,68,29><A,B,2,99,57><B,A,2, 165,49><B,C,1,86,55><C,B,1,39,37>
the sub-chain first reads the transaction information of the round of 10 times, counts the number of times each node participates in the transaction, and simultaneously can read the current type of the node and the type of the previous round of the node from the node updating event, as shown in the following table. The four node types (access node, candidate node, normal node, low efficiency node) are labeled 1-4, respectively.
Firstly, initializing a node feature vector according to historical node information and transaction information of the current round stored in a sub-chain:
as shown in table 1.
TABLE 1 node feature vector
| v 1 ＝[3，2，2，2] |
| v 2 ＝[2，1，4，1] |
| v 3 ＝[2，3，3，1] |
From which a node-feature matrix V is constructed as model input, i.e.
And constructing a weight matrix, a transaction delay matrix and a network delay matrix according to the 10 transaction events of the round.
For the weight matrix, for example, when transaction type r =1, the number of transactions from node a to node B is3, then
The same can be said to be given in Table 2. The same can be said for table 3 when transaction type r =2.
TABLE 2 weight matrix W r (r＝1)
TABLE 3 weight matrix W r (r＝2)
For transaction latency, when r =1, max =86ms, min =25ms, with an interval of
The delay is divided into 5 levels according to this, e.g. average trade delay from node C to node B
The range is divided according to the time delay,
see table 4; the same holds for r =2, from which the intervals are calculated and ranked as shown in table 5.
TABLE 4 transaction delay matrix T r (r＝1)
TABLE 5 transaction delay matrix T r (r＝2)
For network delay, the above steps are respectively used for carrying out delay grade division on different transaction types, as shown in table 6 and table 7.
TABLE 6 network delay matrix L r (r＝1)
TABLE 7 network delay matrix L r (r＝2)
And substituting the defined forward propagation model to obtain the output of the next hidden layer, and obtaining the final state vector of the node after 2 iterations (such as fig. 4).
The classification probability for each node is obtained using Softmax normalization, for example as shown in fig. 5 for node a.
The classification probability values of the node B and the node C obtained in the same manner are shown in tables 8 and 9.
TABLE 8 node B Classification probability values
| 0.31 | Access node |
| 0.43 | Candidate node |
| 0.24 | Common node |
| 0.02 | Inefficient node |
TABLE 9 node C Classification probability values
| 0.03 | Access node |
| 0.05 | Candidate node |
| 0.13 | Common node |
| 0.79 | Inefficient node |
And classifying according to the maximum probability to obtain the node A as an access node, the node B as a candidate node and the node C as a low-efficiency node in the current round. The child chain selects the node A as a communication node accessed to the parent chain according to the classification result, meanwhile, the number of times that the parent chain can count the history of the node A as an inefficient node in a local record is assumed to be 3,3 > 2 (threshold), so that the node A does not meet the condition of being used as an access node, the parent chain requests the child chain to replace the node A with a candidate node B, the child chain selects the node B as the communication node after receiving the request, and the sequencing service establishes a relay chain for data exchange by using the communication node of the parent chain. The sub-chain sends the hash of the processed data of the Internet of things to the main chain through the relay chain, and the data transmission on the chain is completed. Meanwhile, the sub-chain requests a public key of the client from the main chain, the public key is used for encrypting data and then performing down-chain transmission, the main chain receives the data and then decrypts the data by using a private key of the client, a new hash is generated and compared with an on-chain hash, if the hash values are consistent, the data are submitted into the chain and consensus is completed, and communication is finished; if the values are inconsistent, the data are modified in the transmission process, the parent chain discards the values and notifies the child chain, and the child chain can request to upload the data again or end the communication. After the communication is completely finished, the sub-chain waits for the data uplink application of the next piece of internet-of-things equipment, and the flow of the case is repeated.
The invention designs a novel nested chain architecture aiming at the challenge that network data applied under a chain is difficult to upload to a block chain due to mass scale and high real-time performance, realizes the cooperation of information under the chain and data on the chain by utilizing a mechanism of parent-child chain coexistence, provides a child chain node control mechanism integrating sensing, selection and evaluation, and ensures the stable communication between the child chain and the parent chain and the efficient chaining of the data.
The above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: it is to be understood that modifications may be made to the technical solutions described in the foregoing embodiments, or equivalents may be substituted for some of the technical features thereof, but such modifications or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A massive real-time Internet of things-oriented chain loading method based on a novel nested chain architecture is characterized in that a block chain system consists of a single parent chain and a plurality of sub-chains, and the constituent nodes of the parent chain and the sub-chains are equally divided into two types: the common node is only responsible for the local data management function; the communication node has the same function as a common node and also has a cross-link communication function; the communication nodes in the parent chain and the child chain together construct a communication channel, namely a relay chain, which is used for data exchange among the chains; the sub-chain coexists with the parent chain in a nested structure and is used for realizing dynamic sensing of sub-chain communication nodes based on machine learning for large-scale application data under the link, searching malicious nodes, efficiently selecting and replacing parent chain access nodes and realizing high-availability sub-chain dynamic access.
2. The mass real-time internet of things oriented uplink method based on the novel nested chain architecture as claimed in claim 1, wherein the selection process of the sub-chain access nodes is to use an improved GCN algorithm to sense and classify the nodes, so as to screen out efficient nodes in the sub-chain for inter-chain communication.
3. The massive real-time internet of things-oriented uplink method based on the novel nested chain architecture as claimed in claim 2, wherein the selection process of the sub-chain access node is as follows:
s31, graph construction:
constructing transaction graphs by using the characteristic attributes of the nodes in the block chain and the transaction information among the nodes, wherein each transaction graph is a directed graph which is composed of the nodes and edges and contains edge information and is used as the input of a neural network model;
s32, image learning:
the GCN model initially inputs a matrix generated by graph construction, and then node states are updated through hierarchical propagation;
s33, node classification and parent chain collaborative evaluation:
obtaining the state vector of each node after T iterations in the last layer of the GCN model
Wherein t is the output characteristic number of each node, namely the node category number; for each node application
Mapping the model output value into a probability value to obtain the classification probability of each node:
the vector represents the probability that the ith node is in the t categories respectively, namely the probability values of the nodes which are respectively used as an access node, a candidate node, a common node and an inefficient node, and the maximum probability is taken as the node type to obtain the classification probability of each node; meanwhile, the father chain evaluates the access node selected by the sub-chain according to the historical update information of the node, and if the node is the nodeNumber of times T of point history as inefficient node>Threshold Th, the parent chain requests the child chain to replace the access node from the list of candidate nodes.
4. The massive real-time internet of things-oriented uplink method based on the novel nested chain architecture as claimed in claim 3, wherein the graph construction process mainly comprises the following key parameters:
node feature matrix
The characteristic attribute of the kth node is represented as:
f 1 number of transactions initiated for the round, f 2 Number of transactions initiated for previous round, s 1 For the current node type, s 2 Is the type of the node of the previous wheel;
weight matrix
A transaction weight matrix of type r, the weight matrix being defined by transaction weights
Constructing, namely the number of times that a certain edge in the transaction graph is constructed, namely the transaction number from the node i to the node j in the current round;
transaction delay matrix
By
Constructing a value representing the transaction delay level of the type r from the node i to the node j in the M transactions in the current round, and the order of the transaction delay levelSetting n, i.e. there are n delay levels in total, and taking the extreme value t of all transaction delays in the round max 、t min Push-button
Grading uniformly; average transaction delay of type r between node i and node j in the current round
Determining t from a rank-dividing range cur The grade is obtained
Network delay matrix
By
Constructing, wherein the value represents the delay level of the network environment when the transaction with the type r is carried out from the node i to the node j in the M transactions in the current round, the delay level is divided into n levels, and the network ping value t of each transaction with the type r in the current round is counted max 、t min Push-button
Uniformly grading, calculating average network time delay, and determining grade to obtain
5. The mass real-time internet of things oriented uplink method based on the novel nested chain architecture as claimed in claim 3, wherein in the graph learning process, a node forward update propagation model is defined as:
wherein
Representing the hidden state of the ith node in the l +1 layer neural network;
alpha and beta are respectively the weight of transaction delay and network delay, and alpha + beta =1;
σ is a ReLU activation function;
normalization factor
A node set representing that r type transaction is carried out between the current round and the node i;
6. The mass real-time Internet of things-oriented uplink method based on the novel nested chain architecture as claimed in claim 4, wherein in the graph learning process, the iteration number of the state vector is defined as T, i.e. l is less than or equal to T; the input at the model level I is represented as
Wherein H (0) = V, forward propagation takes a total of T state updates.
7. The massive real-time internet of things-oriented uplink method based on the novel nested chain architecture as claimed in any one of claims 1 to 6, comprising the following steps:
s1, original data processing: the method comprises the steps that the Internet of things equipment sends original data to a sub chain through a gateway, firstly, the gateway conducts identity verification on the Internet of things equipment, and after the verification is passed, preprocessing is conducted on the original data before chain linking and the original data are sent to the sub chain;
s2, request sending:
the sub-chain initiates a communication request to the parent chain and sends the request to a system sequencing service;
s3, selecting a communication transaction node:
defining a sub-chain to realize node selection every M times of transactions, and selecting a high-efficiency node by the sub-chain according to situation perception of nodes in the chain to serve as a communication node accessed to a parent chain; the parent chain has a fixed communication node for each sub-chain;
s4, data transmission:
the sequencing service establishes a channel and adds the communication nodes selected by the two parties into the channel to form a relay chain, and the sub-chain communication nodes read specified data from the chain and submit the data to the relay chain after being processed by a specific intelligent contract;
s5, data verification and confirmation:
the two communication nodes in the relay chain reach consensus, data is written into local blocks of the two communication nodes, and the communication node of the parent chain reads the data from the local, and submits the data to the chain and reaches consensus after verifying the integrity and consistency of the received data, so as to complete data exchange;
s6, transaction information storage:
after each transaction is completed, the transaction event is stored in the sub-chain in a key-value pair mode for next node selection, and node updating information is simultaneously sent to the relay chain and then backed up in the parent chain for evaluation of the parent chain on the sub-chain node.
8. The massive real-time internet of things-oriented uplink method based on the novel nested chain architecture as claimed in claim 7, wherein in step S4, the sub-chain uploads only the hash digest of the processed internet of things data to the main chain.
9. The mass real-time internet of things oriented uplink method based on the novel nested chain architecture as claimed in claim 7, wherein in step S6, transaction information attributes are as follows: < sender, receiver, type, transaction delay, network delay >, sender represents a transaction initiator, receiver represents a transaction receiver, type represents a transaction type, transaction delay represents a transaction delay (in ms), and network delay represents a network delay.
10. The mass real-time internet of things oriented uplink method based on the novel nested chain architecture as claimed in claim 9, wherein if the transaction passes through a sub-chain node selection process, a type update event of each node is stored in a sub-chain in a key value pair form, the attribute includes < ID, category, transactions >, the ID represents a node ID, the category represents a node type after the update, and the transactions represents a transaction number initiated by the node of the current round.
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